# Towards automated brain aneurysm detection in TOF-MRA: open data, weak labels, and anatomical knowledge

arXiv: https://arxiv.org/abs/2103.06168 · HTML: https://mlsanity.com/paper/2103.06168/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 47 references that are in the map.
- First posted on arXiv 10 Mar 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Deep Learning for Medical Image Processing: Overview, Challenges and Future](https://mlsanity.com/paper/1704.06825/) (2017)
- [Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation](https://mlsanity.com/paper/1805.02798/) (2018)
- [Coarse-to-fine volumetric segmentation of teeth in Cone-Beam CT](https://mlsanity.com/paper/1810.10293/) (2018)
- [An anatomically-informed 3D CNN for brain aneurysm classification with weak labels](https://mlsanity.com/paper/2012.08645/) (2020)

## Built on by (most cited first, 12 of 12)

- [The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography](https://mlsanity.com/paper/2312.17670/) (2023)
- [Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario](https://mlsanity.com/paper/2411.02477/) (2024)
- [Automated detection of motion artifacts in brain MR images using deep learning and explainable artificial intelligence](https://mlsanity.com/paper/2402.08749/) (2024)
- [AneumoBench: A Source-Linked Benchmark for Synthetic-Geometry Transfer in Aneurysm CFD](https://mlsanity.com/paper/2505.14717/) (2025)
- [Weakly Supervised Intracranial Aneurysm Detection and Segmentation in MR angiography via Multi-task UNet with Vesselness Prior](https://mlsanity.com/paper/2508.00235/) (2025)
- [Weakly supervised segmentation of intracranial aneurysms using a novel 3D focal modulation UNet](https://mlsanity.com/paper/2308.03001/) (2023)
- [A vascular synthetic model for improved aneurysm segmentation and detection via Deep Neural Networks](https://mlsanity.com/paper/2403.18734/) (2024)
- [Anatomically-guided masked autoencoder pre-training for aneurysm detection](https://mlsanity.com/paper/2502.21244/) (2025)
- [Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: a multi-reader study](https://mlsanity.com/paper/2503.17786/) (2025)
- [Hessian-Based Lightweight Neural Network HessNet for State-of-the-Art Brain Vessel Segmentation on a Minimal Training Dataset](https://mlsanity.com/paper/2508.15660/) (2025)
- [VesselBridge3D: A Foundation Model Adaptation Framework for Label-Efficient 3D Vessel Segmentation](https://mlsanity.com/paper/2602.23782/) (2026)
- [Intracranial Aneurysm Classification and Segmentation via Tri-Axial ROI and Multi-Task Learning](https://mlsanity.com/paper/2606.26706/) (2026)

## Abstract (arXiv)

Brain aneurysm detection in Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) has undergone drastic improvements with the advent of Deep Learning (DL). However, performances of supervised DL models heavily rely on the quantity of labeled samples, which are extremely costly to obtain. Here, we present a DL model for aneurysm detection that overcomes the issue with ''weak'' labels: oversized annotations which are considerably faster to create. Our weak labels resulted to be four times faster to generate than their voxel-wise counterparts. In addition, our model leverages prior anatomical knowledge by focusing only on plausible locations for aneurysm occurrence. We frst train and evaluate our model through cross-validation on an in-house TOF-MRA dataset comprising 284 subjects (170 females / 127 healthy controls / 157 patients with 198 aneurysms). On this dataset, our best model achieved a sensitivity of 83%, with False Positive (FP) rate of 0.8 per patient. To assess model generalizability, we then participated in a challenge for aneurysm detection with TOF-MRA data (93 patients, 20 controls, 125 aneurysms). On the public challenge, sensitivity was 68% (FP rate=2.5), ranking 4th/18 on the open leaderboard. We found no signifcant diference in sensitivity between aneurysm risk-of-rupture groups (p=0.75), locations (p=0.72), or sizes (p=0.15). Data, code and model weights are released under permissive licenses. We demonstrate that weak labels and anatomical knowledge can alleviate the necessity for prohibitively expensive voxel-wise annotations.